Abstract
Extended-range runoff forecasting is important for water resources management and energy planning. Experimental extended-range runoff was hindcasted, based on an extended-range climate model, developed by National Climate Center of the China Meteorological Administration, and semi-distributed hydrological model HBV-D. The skill of the runoff forecasts was explored using mean square skill score (MSSS), anomaly correlation coefficient (ACC), and areas under the relative operating characteristics curve (AUC) for three terciles for three experimental 51-day periods during flood season (June 1 to July 21, July 1 to August 20 and August 1 to September 20) for two rivers in China. The results revealed decreasing trends of the five indices, and varying length of the continuous longest skilful time slice from 3 days to 6 weeks depending on index, period and river location. In most cases, skilful abnormal terciles forecast occurred more often or with similar frequency to deterministic forecasts. It suggests that ensemble probability forecasting is a method with potential for extended-range river runoff forecast. Further, abnormal terciles are more skillful than normal terciles, and above normal are more skillful than below normal. In terms of a temporal mean of the MSSS and ACC, deterministic forecasts are skillful for both rivers in all three periods, but more skillful for the Beijiang River than for the Yiluo River in most cases.
Highlights
Reliable and timely runoff forecasts have potential to provide critical information with lead times ranging from minutes to years
The HBV-D model is shown well able to simulate satisfactory in relation to the Yiluo and Beijiang rivers (Table 2),2 i.e., both Nash–Sutcliffe efficiency (NSE) and R2 are >0.5 and monthly runoff using gridded climate forcing (NSE > 0.73, R > 0.80 and percentage bias (PBIAS) < ±15%) during
The maximum and minimum values of Decreasing trends of the five indices were observed during the three 51-day periods, and the length of the skilful time slices varied with forecast period and geographic location
Summary
Reliable and timely runoff forecasts have potential to provide critical information with lead times ranging from minutes to years. Such information is vital for numerous users, e.g., the emergency services, hydropower generators, irrigators, and rural and urban water supply authorities, as well as environmental managers in their role of mitigating disasters, controlling hydropower activities, determining industrial, domestic and agricultural water allocations and protecting the environment [1,2,3,4]. To meet the requirements of various users, in past half decades, couples of hydrological forecast models and forecasting systems have been proposed and developed.
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